SoroushVahidi/scidocs huggingface dataset:
Partitioning graphs to speedup Dijkstra's algorithm
(2005)

We study an acceleration method for point-to-point shortest-path computations in large and sparse directed graphs with given nonnegative arc weights. The acceleration method is called the arc-flag approach and is based on Dijkstra's algorithm. In the arc-flag approach, we allow a preprocessing of the network data to generate additional information, which is then used to speedup shortest-path queries. In the preprocessing phase, the graph is divided into regions and information is gathered on whether an arc is on a shortest path into a given region. The arc-flag method combined with an appropriate partitioning and a bidirected search achieves an average speedup factor of more than 500 compared to the standard algorithm of Dijkstra on large networks (1 million nodes, 2.5 million arcs). This combination narrows down the search space of Dijkstra's algorithm to almost the size of the corresponding shortest path for long-distance shortest-path queries. We conduct an experimental study that evaluates which partitionings are best suited for the arc-flag method. In particular, we examine partitioning algorithms from computational geometry and a multiway arc separator partitioning. The evaluation was done on German road networks. The impact of different partitions on the speedup of the shortest path algorithm are compared. Furthermore, we present an extension of the speedup technique to multiple levels of partitions. With this multilevel variant, the same speedup factors can be achieved with smaller space requirements. It can, therefore, be seen as a compression of the precomputed data that preserves the correctness of the computed shortest paths.
Porcelain laminate veneers: 6- to 12-year clinical evaluation--a retrospective study.
(2005)

The purpose of this study was to retrospectively evaluate the clinical performance of laminate veneers placed in the anterior segments of the dental arches over a 12-year period at two different private dental practices. Forty-six patients were restored with 182 porcelain laminate veneers. The veneers were studied for a mean observation time of 5.69 years. Color match, porcelain surface, marginal discoloration, and marginal integrity were clinically examined following modified CDA/Ryge criteria. On the basis of the criteria used, most of the veneers rated A. Risk of fracture was determined with a Kaplan-Meier survival analysis. Probability of survival of the 182 veneers was 94.4% at 12 years, with a low clinical failure rate (approximately 5.6%). Porcelain veneers must be bonded with a correct adhesive technique to reach this successful survival rate.
Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies
(2017)

OBJECTIVE
Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies.


METHODS
From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations.


RESULTS
We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning.


CONCLUSION
Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation.
Continuous Auctions and Insider Trading
(2007)

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Simulating cartoon style animation
(2002)

Traditional hand animation is in many cases superior to simulated motion for conveying information about character and events. Much of this superiority comes from an animator's ability to abstract motion and play to human perceptual effects. However, experienced animators are difficult to come by and the resulting motion is typically not interactive. On the other hand, procedural models for generating motion, such as physical simulation, can create motion on the fly but are poor at stylizing movement. We start to bridge this gap with a technique that creates cartoon style deformations automatically while preserving desirable qualities of the object's appearance and motion. Our method is focused on squash-and-stretch deformations based on the velocity and collision parameters of the object, making it suitable for procedural animation systems. The user has direct control of the object's motion through a set of simple parameters that drive specific features of the motion, such as the degree of squash and stretch. We demonstrate our approach with examples from our prototype system.
Multilevel resilience analysis of transportation and communication networks
(2015)

For many years the research community has attempted to model the Internet in order to better understand its behaviour and improve its performance. Since much of the structural complexity of the Inter*Work performed while Egemen K. Çetinkaya, Andrew M. Peck, and Justin P. Rohrer were at the University of Kansas. Egemen K. Çetinkaya Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA cetinkayae@mst.edu, +1 573 341 6887 Mohammed J.F. Alenazi Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA malenazi@ittc.ku.edu Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia Andrew M. Peck apeck@ittc.ku.edu; andrewpeck11@gmail.com Justin P. Rohrer rohrej@ittc.ku.edu Department of Computer Science, Naval Postgraduate School, Monterey, CA 93943–5285, jprohrer@nps.edu James P.G. Sterbenz Department of Electrical Engineering and Computer Science, Information and Telecommunication Technology Center, The University of Kansas, Lawrence, KS 66045, USA https://www.ittc.ku.edu/resilinets jpgs@ittc.ku.edu, +1 508 944 3067 School of Computing and Communications, InfoLab21 Lancaster University, Lancaster, UK, jpgs@comp.lancs.ac.uk Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong jpgs@comp.polyu.edu.hk net is due to its multilevel operation, the Internet’s multilevel nature is an important and non-trivial feature that researchers must consider when developing appropriate models. In this paper, we compare the normalised Laplacian spectra of physicaland logical-level topologies of four commercial ISPs and two research networks against the US freeway topology, and show analytically that physical level communication networks are structurally similar to the US freeway topology. We also generate synthetic Gabriel graphs of physical topologies and show that while these synthetic topologies capture the grid-like structure of actual topologies, they are more expensive than the actual physical level topologies based on a network cost model. Moreover, we introduce a distinction between geographic graphs that include degree-2 nodes needed to capture the geographic paths along which physical links follow, and structural graphs that eliminate these degree-2 nodes and capture only the interconnection properties of the physical graph and its multilevel relationship to logical graph overlays. Furthermore, we develop a multilevel graph evaluation framework and analyse the resilience of single and multilevel graphs using the flow robustness metric. We then confirm that dynamic routing performed over the lower levels helps to improve the performance of a higher level service, and that adaptive challenges more severely impact the performance of the higher levels than non-adaptive challenges.
Hand Gesture Recognition with Leap Motion
(2018)

The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multiclass SVM classifier to recognize performed gesture, dimension reduction and feature weighted fusion are also discussed. Our results show that our model is much more accurate than previous work.
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial Optimization
(2015)

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Generative Ad-versarial Networks (GAN) are generative neural networks which can be trained to implicitly model the probability distribution of given data, and it is possible to sample this distribution. We integrate a GAN into an EDA and evaluate the performance of this system when solving combinatorial optimization problems with a single objective. We use several standard benchmark problems and compare the results to state-of-the-art multivariate EDAs. GAN-EDA doe not yield competitive results – the GAN lacks the ability to quickly learn a good approximation of the probability distribution. A key reason seems to be the large amount of noise present in the first EDA generations.
Discovery of significant parameters in kidney dialysis data sets by K-means algorithm
(2014)

The contributing factors for kidney dialysis such as creatinine, sodium, urea plays an important role in deciding the survival prediction of the patients as well as the need for undergoing kidney transplantation. Several attempts have been made to derive automated decision making procedure for earlier prediction. This preliminary study investigates the importance of clustering technique for identifying the influence of kidney dialysis parameters. A simple K-means algorithm is used to elicit knowledge about the interaction between many of these measured parameters and patient survival. The clustering procedure predicts the survival period of the patients who is undergoing the dialysis procedure.
Vascular Complications After Chin Augmentation Using Hyaluronic Acid
(2017)

Vascular complications after hyaluronic acid (HA) filling of the chin have rarely been reported. In this report, two cases of vascular occlusion after HA augmentation of the mentum are presented. The first case involved local skin necrosis that resulted from a massive microcirculatory embolism and/or external compression of the chin skin microvasculature. The second case involved vascular compromise in the tongue that resulted from HA injection in the chin. The diagnosis was established on the basis of interventional angiography findings. Concerning the pathogenesis, we hypothesized that the filler embolus flowed into the branches of the deep lingual artery through the rich vascular anastomoses among the submental, sublingual, and deep lingual arteries, after being accidentally injected into the submental artery (or its branches). Level of Evidence V This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
Virtual Notepad: handwriting in immersive VR
(1998)

We present Virtual Notepad, a collection of interface tools that allows the user to take notes, annotate documents and input text using a pen, while still immersed in virtual environments (VEs). Using a spatially-tracked, pressure-sensitive graphics tablet, pen and handwriting recognition software, Virtual Notepad explores handwriting as a new modality for interaction in immersive VEs. This paper reports details of the Virtual Notepad interface and interaction techniques, discusses implementation and design issues, reports the results of initial evaluation and overviews possible applications of virtual handwriting.
Food Addiction and Bulimia Nervosa: New Data Based on the Yale Food Addiction Scale 2.0.
(2016)

Previous research on 'food addiction' as measured with the Yale Food Addiction Scale (YFAS) showed a large overlap between addiction-like eating and bulimia nervosa. Most recently, a revised version of the YFAS has been developed according to the changes made in the diagnostic criteria for substance use disorder in the Diagnostic and Statistical Manual of Mental Disorders fifth edition. The current study examined prevalence and correlates of the YFAS2.0 in individuals with bulimia (n = 115) and controls (n = 341). Ninety-six per cent of participants with bulimia and 14% of controls received a YFAS2.0 diagnosis. A higher number of YFAS2.0 symptoms was associated with lower interoceptive awareness, higher depressiveness, and higher impulsivity in both groups. However, a higher number of YFAS2.0 symptoms was associated with higher body mass and weight suppression in controls only and not in participants with bulimia. The current study is the first to show a large overlap between bulimia and 'food addiction' as measured with the YFAS2.0, replicating and extending findings from studies, which used the previous version of the YFAS. Compensatory weight control behaviours in individuals with bulimia likely alleviate the association between addiction-like eating and higher body mass. Thus, the large overlap between bulimia and 'food addiction' should be taken into consideration when examining the role of addiction-like eating in weight gain and obesity. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.
Database forensic analysis through internal structure carving
(2015)

Forensic tools assist analysts with recovery of both the data and system events, even from corrupted storage. These tools typically rely on “file carving” techniques to restore files after metadata loss by analyzing the remaining raw file content. A significant amount of sensitive data is stored and processed in relational databases thus creating the need for database forensic tools that will extend file carving solutions to the database realm. Raw database storage is partitioned into individual “pages” that cannot be read or presented to the analyst without the help of the database itself. Furthermore, by directly accessing raw database storage, we can reveal things that are normally hidden from database users. There exists a number of database-specific tools developed for emergency database recovery, though not usually for forensic analysis of a database. In this paper, we present a universal tool that seamlessly supports many different databases, rebuilding table and other data content from any remaining storage fragments on disk or in memory. We define an approach for automatically (with minimal user intervention) reverse engineering storage in new databases, for detecting volatile data changes and discovering user action artifacts. Finally, we empirically verify our tool's ability to recover both deleted and partially corrupted data directly from the internal storage of different databases. © 2015 The Authors. Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/
Software Defined Wireless Networks: A Survey of Issues and Solutions
(2017)

Wireless networks such as mobile networks, with their inflexible and expensive network infrastructure, are facing various challenges in efficiently handling the exponentially growing traffic demands of users. Hence, mobile network operators (MNOs) are looking forward to novel networking paradigms which could simplify the task of network management and control and allow faster deployment of newer solutions on top of existing hardware by software updates. Software defined networking (SDN) is a revolutionary technology which makes networks more agile and flexible by separation of data plane and control plane tasks. SDN is playing a key role in deploying mobile network services on network function virtualization (NFV) infrastructure for elastic and flexible deployment of core network services e.g., virtualization of evolved packet core in LTE to efficiently handle huge control signal overhead and traffic demands from machine-to-machine and internet of things devices. Besides, NFV and SDN are offering scalable, flexible and adaptable network service chaining platforms as a replacement for inflexible middleboxes. Mobile edge computing (MEC) is a new evolving platform which brings together IT services and cloud computing for offering end-users network aware services and solutions. In this survey, we enumerate numerous issues and challenges in designing SDN based wireless networks and review various SDN based seminal solutions for 4G/5G. Finally, this survey presents the role of SDN, NFV and MEC in designing 5G networks.
Improving Storage System Reliability with Proactive Error Prediction
(2017)

This paper proposes the use of machine learning techniques to make storage systems more reliable in the face of sector errors. Sector errors are partial drive failures, where individual sectors on a drive become unavailable, and occur at a high rate in both hard disk drives and solid state drives. The data in the affected sectors can only be recovered through redundancy in the system (e.g. another drive in the same RAID) and is lost if the error is encountered while the system operates in degraded mode, e.g. during RAID reconstruction. In this paper, we explore a range of different machine learning techniques and show that sector errors can be predicted ahead of time with high accuracy. Prediction is robust, even when only little training data or only training data for a different drive model is available. We also discuss a number of possible use cases for improving storage system reliability through the use of sector error predictors. We evaluate one such use case in detail: We show that the mean time to detecting errors (and hence the window of vulnerability to data loss) can be greatly reduced by adapting the speed of a scrubber based on error predictions.
Planar Ultra Wideband bandpass filter using Defected Ground Structure
(2015)

The design of planar Ultra Wide Band (UWB) band pass filter using periodic Defected Ground Structure (DGS) is presented. The filter is realized with interdigital coupled lines, stepped impedance open stub and slotted DGS. Interdigital coupled lines are used to widen the band width of the filter. Stub is to provide transmission zeros in upper and lower stop band where as DGS improves the return loss of the filter. It provides an insertion loss of 0.3 dB with a wide pass band from 3.1 - 10.6 GHz. It also provides better return loss of 19 dB.
Naive Bayes for Text Classification with Unbalanced Classes
(2006)

Abstract. Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1, 2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.
Texture synthesis using convolutional neural networks with long-range consistency and spectral constraints
(2016)

Procedural texture generation enables the creation of more rich and detailed virtual environments without the help of an artist. However, finding a flexible generative model of real world textures remains an open problem. We present a novel Convolutional Neural Network based texture model consisting of two summary statistics (the Gramian and Translation Gramian matrices), as well as spectral constraints. We investigate the Fourier Transform or Window Fourier Transform in applying spectral constraints, and find that the Window Fourier Transform improved the quality of the generated textures. We demonstrate the efficacy of our system by comparing generated output with that of related state of the art systems.
Properties and performance of a center/surround retinex
(1997)

The last version of Land's (1986) retinex model for human vision's lightness and color constancy has been implemented and tested in image processing experiments. Previous research has established the mathematical foundations of Land's retinex but has not subjected his lightness theory to extensive image processing experiments. We have sought to define a practical implementation of the retinex without particular concern for its validity as a model for human lightness and color perception. We describe the trade-off between rendition and dynamic range compression that is governed by the surround space constant. Further, unlike previous results, we find that the placement of the logarithmic function is important and produces best results when placed after the surround formation. Also unlike previous results, we find the best rendition for a "canonical" gain/offset applied after the retinex operation. Various functional forms for the retinex surround are evaluated, and a Gaussian form is found to perform better than the inverse square suggested by Land. Images that violate the gray world assumptions (implicit to this retinex) are investigated to provide insight into cases where this retinex fails to produce a good rendition.